Senior Transformer AI Engineer (Remote)

Mira Ai LLC; United States; Remote; USD 150,000 - 230,000; Full time

Benefits offered by this job

Remote work option

Job summary

Mira Ai LLC is seeking a lead researcher/engineer to drive transformer-based language models, spanning design, large-scale training, optimization, and alignment for production readiness. The role requires collaboration across teams and sharing research via publications.

You will design and implement transformer architectures, lead research projects, and improve model performance while reducing computational demands, applying safety practices throughout development and deployment with partner

Qualifications

• Master’s or Ph.D. in computer science, ML, or related field.
• Experience with transformer architectures (GPT/BERT/T5).
• Proficiency in Python and DL frameworks (PyTorch or TensorFlow).
• Experience with distributed training and large-scale model training.
• Strong NLP and deep learning expertise with publications or equivalent work.

Responsibilities

• Design, implement, and optimize transformer neural network architectures.
• Lead research projects advancing language model capabilities.
• Develop methods to improve performance while reducing computational demands.
• Apply alignment, safety, and ethical practices to model development.
• Collaborate with partner teams to productionize research improvements.
• Track research and publish findings in top conferences or journals.

Skills

Transformer architectures

NLP & DL

Python

Distributed training

Model optimization

Research publications

Education

Master’s or Ph.D. in CS/ML

Tools

PyTorch

TensorFlow

Job description

Mira Ai LLC is seeking a lead researcher/engineer to drive transformer-based language models, spanning design, large-scale training, optimization, and alignment for production readiness. The role requires collaboration across teams and sharing research via publications.

You will design and implement transformer architectures, lead research projects, and improve model performance while reducing computational demands, applying safety practices throughout development and deployment with partner